MocapMe: DeepLabCut-Enhanced Neural Network for Enhanced Markerless Stability in Sit-to-Stand Motion Capture.

This study examined the efficacy of an optimized DeepLabCut (DLC) model in motion capture, with a particular focus on the sit-to-stand (STS) movement, which is crucial for assessing the functional capacity in elderly and postoperative patients. This research uniquely compared the performance of this optimized DLC model, which was trained using 'filtered' estimates from the widely used OpenPose (OP) model, thereby emphasizing computational effectiveness, motion-tracking precision, and enhanced stability in data capture. Utilizing a combination of smartphone-captured videos and specifically curated datasets, our methodological approach included data preparation, keypoint annotation, and extensive model training, with an emphasis on the flow of the optimized model. The findings demonstrate the superiority of the optimized DLC model in various aspects. It exhibited not only higher computational efficiency, with reduced processing times, but also greater precision and consistency in motion tracking thanks to the stability brought about by the meticulous selection of the OP data. This precision is vital for developing accurate biomechanical models for clinical interventions. Moreover, this study revealed that the optimized DLC maintained higher average confidence levels across datasets, indicating more reliable and accurate detection capabilities compared with standalone OP. The clinical relevance of these findings is profound. The optimized DLC model's efficiency and enhanced point estimation stability make it an invaluable tool in rehabilitation monitoring and patient assessments, potentially streamlining clinical workflows. This study suggests future research directions, including integrating the optimized DLC model with virtual reality environments for enhanced patient engagement and leveraging its improved data quality for predictive analytics in healthcare. Overall, the optimized DLC model emerged as a transformative tool for biomechanical analysis and physical rehabilitation, promising to enhance the quality of patient care and healthcare delivery efficiency.

Do patients with neurological disorders benefit from immersive virtual reality? A scoping review on the emerging use of the computer-assisted rehabilitation environment.

INTRODUCTION: Virtual reality (VR) is an advanced technology that creates simulated environments and conditions. By offering the possibility of combining motor, cognitive, and well-being in conjunction with the potential to manipulate multi-sensorial features in a safe environment, VR has emerged as a promising powerful rehabilitation tool. Among advanced VR systems, various authors have highlighted promising effects in the rehabilitation of the computer-assisted rehabilitation environment (CAREN - Motekforce Link; Amsterdam, The Netherlands). In our scoping review, we aimed to map the existing evidence on the use of CAREN in the rehabilitation of neurological patients. EVIDENCE ACQUISITION: This scoping review was conducted following the PRISMA guidelines. A search was carried out for all peer-reviewed articles published until June 30, 2023, using the following databases: PubMed, Embase, Cochrane Database, PeDro and Web of Science. The following terms have been used: ("Cognitive Rehabilitation" OR "Motor Rehabilitation" OR "CAREN" or "Computer-Assisted Rehabilitation Environment") AND ("Virtual Reality" OR "Rehab"). EVIDENCE SYNTHESIS: From the assessed studies, only seven met the inclusion criteria: 1) one study concerned cognitive rehabilitation in patients suffering from Parkinson's Disease (PD); 2) one was on the usability of CAREN in PD patients; 3) two studies related to the influence of emotional components to CAREN rehabilitation; 4) three studies were related to motor rehabilitation using CAREN, and involved individuals with PD, Multiple Sclerosis, TBI, respectively. Generally, the few assessed studies demonstrate that CAREN is a safe and potentially effective tool to treat different symptoms (including gait and vestibular disturbances, executive function, depressive mood, and anxiety) in patients with different neurological disorders. CONCLUSIONS: The reviewed literature indicated the potential use of CAREN in improving motor and cognitive skills with conflicting results on emotional aspects. However, since the data comes from few and small sample size studies, further research is needed to confirm the effectiveness of the tool in neurorehabilitation.

Machine learning to detect, stage and classify diseases and their symptoms based on inertial sensor data: a mapping review.

This article presents a systematic review aimed at mapping the literature published in the last decade on the use of machine learning (ML) for clinical decision-making through wearable inertial sensors. The review aims to analyze the trends, perspectives, strengths, and limitations of current literature in integrating ML and inertial measurements for clinical applications. The review process involved defining four research questions and applying four relevance assessment indicators to filter the search results, providing insights into the pathologies studied, technologies and setups used, data processing schemes, ML techniques applied, and their clinical impact. When combined with ML techniques, inertial measurement units (IMUs) have primarily been utilized to detect and classify diseases and their associated motor symptoms. They have also been used to monitor changes in movement patterns associated with the presence, severity, and progression of pathology across a diverse range of clinical conditions. ML models trained with IMU data have shown potential in improving patient care by objectively classifying and predicting motor symptoms, often with a minimally encumbering setup. The findings contribute to understanding the current state of ML integration with wearable inertial sensors in clinical practice and identify future research directions. Despite the widespread adoption of these technologies and techniques in clinical applications, there is still a need to translate them into routine clinical practice. This underscores the importance of fostering a closer collaboration between technological experts and professionals in the medical field.

Optimal Identification of Muscle Synergies From Typical Sit-to-Stand Clinical Tests.

Goal: The goal of this manuscript is to investigate the optimal methods for extracting muscle synergies from a sit-to-stand test; in particular, the performance in identifying the modular structures from signals of different length is characterized. Methods: Surface electromyography signals have been recorded from instrumented sit-to-stand trials. Muscle synergies have then been extracted from signals of different duration (i.e. 5 times sit to stand and 30 seconds sit to stand) from different portions of a complete sit-to-stand-to-sit cycle. Performance have then been characterized using cross-validation procedures. Moreover, an optimal method based on a modified Akaike Information Criterion measure is applied on the signal for selecting the correct number of synergies from each trial. Results: Results show that it is possible to identify correctly muscle synergies from relatively short signals in a sit-to-stand experiment. Moreover, the information about motor control structures is identified with a higher consistency when only the sit-to-stand phase of the complete cycle is considered. Conclusions: Defining a set of optimal methods for the extraction of muscle synergies from a clnical test such as the sit-to-stand is of key relevance to ensure the applicability of any synergy-related analysis in the clinical practice, without requiring knowledge of the technical signal processing methods and the underlying features of the signal.

Characterization of prosthetic knees through a low-dimensional description of gait kinematics.

The characterization of both limbs' behaviour in prosthetic gait is of key importance for improving the prosthetic components and increasing the biomechanical capability of trans-femoral amputees. When characterizing human gait, modular motor control theories have been proven to be powerful in providing a compact description of the gait patterns. In this paper, the planar covariation law of lower limb elevation angles is proposed as a compact, modular description of prosthetic gait; this model is exploited for a comparison between trans-femoral amputees walking with different prosthetic knees and control subjects walking at different speeds. Results show how the planar covariation law is maintained in prostheses users, with a similar spatial organization and few temporal differences. Most of the differences among the different prosthetic knees are found in the kinematic coordination patterns of the sound side. Moreover, different geometrical parameters have been calculated over the common projected plane, and their correlation with classical gait spatiotemporal and stability parameters has been investigated. The results from this latter analysis have highlighted a correlation with several parameters of gait, suggesting that this compact description of kinematics unravels a significant biomechanical meaning. These results can be exploited to guide the control mechanisms of prosthetic devices based purely on the measurement of relevant kinematic quantities.

Predictive simulation of sit-to-stand based on reflexive-controllers.

Sit-to-stand can be defined as a set of movements that allow humans to rise from a sitting position to a bipedal standing pose. These movements, often categorized as four distinct kinematic phases, must be coordinated for assuring personal autonomy and can be compromised by ageing or physical impairments. To solve this, rehabilitation techniques and assistive devices demand proper description of the principles that lead to the correct completion of this motor task. While the muscular dynamics of the sit-to-stand task have been analysed, the underlying neural activity remains unknown and largely inaccessible for conventional measurement systems. Predictive simulations can propose motor controllers whose plausibility is evaluated through the comparison between simulated and experimental kinematics. In the present work, we modelled an array of reflexes that originate muscle activations as a function of proprioceptive and vestibular feedback. This feedback encodes torso position, displacement velocity and acceleration of a modelled human body with 7 segments, 9 degrees of freedom, and 50 actuators. We implemented two controllers: a four-phases controller where the reflex gains and composition vary depending on the kinematic phase, and a simpler two-phases controller, where three of the kinematic phases share the same reflex gains. Gains were optimized using Covariance Matrix Adaptation. The results of the simulations reveal, for both controllers, human-like sit-to-stand movement, with joint angles and muscular activity comparable to experimental data. The results obtained with the simplified two-phases controller indicate that a simple set of reflexes could be sufficient to drive this motor task.

Characterizing the Gait of People With Different Types of Amputation and Prosthetic Components Through Multimodal Measurements: A Methodological Perspective.

Prosthetic gait implies the use of compensatory motor strategies, including alterations in gait biomechanics and adaptations in the neural control mechanisms adopted by the central nervous system. Despite the constant technological advancements in prostheses design that led to a reduction in compensatory movements and an increased acceptance by the users, a deep comprehension of the numerous factors that influence prosthetic gait is still needed. The quantitative prosthetic gait analysis is an essential step in the development of new and ergonomic devices and to optimize the rehabilitation therapies. Nevertheless, the assessment of prosthetic gait is still carried out by a heterogeneous variety of methodologies, and this limits the comparison of results from different studies, complicating the definition of shared and well-accepted guidelines among clinicians, therapists, physicians, and engineers. This perspective article starts from the results of a project funded by the Italian Worker's Compensation Authority (INAIL) that led to the generation of an extended dataset of measurements involving kinematic, kinetic, and electrophysiological recordings in subjects with different types of amputation and prosthetic components. By encompassing different studies published along the project activities, we discuss the specific information that can be extracted by different kinds of measurements, and we here provide a methodological perspective related to multimodal prosthetic gait assessment, highlighting how, for designing improved prostheses and more effective therapies for patients, it is of critical importance to analyze movement neural control and its mechanical actuation as a whole, without limiting the focus to one specific aspect.

Classifying reaching height through muscle synergies in unconstrained scenarios.

Muscle synergy analysis has been widely adopted in the literature for the analysis of upper limb surface electromyographic signals during reaching tasks and for the prediction of movement direction for myoelectric control purposes. However, previous studies have characterized movements in constrained or semi-constrained scenarios, in which the subjects performing the movement were instructed to reach particular targets or were given some kind of feedback. In this work, the same synergy model has been applied to a completely unconstrained upper limb reaching experiment, with the aim of classifying the height of the target starting from the activity of the synergies. Results show that the synergistic model is able to extract compact features that can identify with good performance three different reaching heights. Moreover, this representation is able to isolate the signals that contain predictive information about the movement direction from the ones that are related to movement timing; this, together with the good performance of the synergy-based classifier supports the proposal of applying this model to the pre-processing of electromyographic signals when dealing with control systems that use signals from multiple muscles to predict movements.

The Influence of the sEMG Amplitude Estimation Technique on the EMG-Force Relationship.

The estimation of the sEMG-force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths.

An Objective, Information-Based Approach for Selecting the Number of Muscle Synergies to be Extracted via Non-Negative Matrix Factorization.

Muscle synergy analysis is a useful tool for the evaluation of the motor control strategies and for the quantification of motor performance. Among the parameters that can be extracted, most of the information is included in the rank of the modular control model (i.e. the number of muscle synergies that can be used to describe the overall muscle coordination). Even though different criteria have been proposed in literature, an objective criterion for the model order selection is needed to improve reliability and repeatability of MSA results. In this paper, we propose an Akaike Information Criterion (AIC)-based method for model order selection when extracting muscle synergies via the original Gaussian Non-Negative Matrix Factorization algorithm. The traditional AIC definition has been modified based on a correction of the likelihood term, which includes signal dependent noise on the neural commands, and a Discrete Wavelet decomposition method for the proper estimation of the number of degrees of freedom of the model, reduced on a synergy-by-synergy and event-by-event basis. We tested the performance of our method in comparison with the most widespread ones, proving that our criterion is able to yield good and stable performance in selecting the correct model order in simulated EMG data. We further evaluated the performance of our AIC-based technique on two distinct experimental datasets confirming the results obtained with the synthetic signals, with performances that are stable and independent from the nature of the analysed task, from the signal quality and from the subjective EMG pre-processing steps.

Reorganization of Muscle Coordination Underlying Motor Learning in Cycling Tasks.

The hypothesis of modular control, which stands on the existence of muscle synergies as building blocks of muscle coordination, has been investigated in a great variety of motor tasks and species. Yet, its role during learning processes is still largely unexplored. To what extent is such modular control flexible, in terms of spatial structure and temporal activation, to externally or internally induced adaptations, is a debated issue. To address this question, we designed a biofeedback experiment to induce changes in the timing of muscle activations during leg cycling movements. The protocol consisted in delaying the peak of activation of one target muscle and using its electromyography (EMG) envelope as visual biofeedback. For each of the 10 healthy participants, the protocol was repeated for three different target muscles: Tibialis Anterioris (TA), Gastrocnemius Medialis (GM), and Vastus Lateralis (VL). To explore the effects of the conditioning protocol, we analyzed changes in the activity of eight lower limb muscles by applying different models of modular motor control [i.e., fixed spatial components (FSC) and fixed temporal components (FTC)]. Our results confirm the hypothesis that visual EMG biofeedback is able to induce changes in muscle coordination. Subjects were able to shift the peak of activation of the target muscle, with a delay of (49 ± 27°) across subjects and conditions. This time shift generated a reorganization of all the other muscles in terms of timing and amplitude. By using different models of modular motor control, we demonstrated that neither spatially invariant nor temporally invariant muscle synergies alone were able to account for these changes in muscle coordination after learning, while temporally invariant muscle synergies with adjustments in timing could capture most of muscle activity adaptations observed after the conditioning protocol. These results suggest that short-term learning in rhythmic tasks is built upon synergistic temporal commands that are robust to changes in the task demands.

Pelvic obliquity as a compensatory mechanism leading to lower energy recovery: Characterization among the types of prostheses in subjects with transfemoral amputation.

BACKGROUND: Subjects with transfemoral amputation (TFA) show an asymmetric gait pattern associated with a decreased ability to recover mechanical energy and an increased metabolic cost of walking. RESEARCH QUESTION: This study aimed to identify the spatio-temporal and kinematic gait variables correlated with mechanical energy values in subjects with TFA and to observe the ability of the identified parameters to discriminate between TFA and controls according to the type of prosthesis. METHODS: The gait of 40 subjects with TFA was evaluated with a motion 3-D optoelectronic system. Nine subjects wore a mechanical prosthesis (TFAm), seventeen a C-Leg prosthesis (TFAc), and fourteen a Genium prosthesis (TFAg). Spatio-temporal and pelvic kinematic parameters were measured. Energy recovery was measured relative to the whole-body center of mass (CoM) kinematics as the fraction of mechanical energy recovered during each walking step (R-step). Correlation tests and multiple linear regression analyses were used to evaluate the correlation and association between kinematic and energy variables, respectively. Receiver operating characteristics curves were plotted to assess the ability of the correlated parameter to distinguish subjects with TFA from controls, and optimal cutoff point values were calculated according to the type of prosthesis. RESULTS: Among the spatio-temporal and kinematic parameters correlated to R-step, only pelvic obliquity of the prosthetic side was significantly associated with R-step. It showed an excellent ability to discriminate between TFA and controls. Furthermore, pelvic obliquity showed an excellent discriminative ability in identifying TFAm and TFAc and a good discriminative ability in identifying TFAg from controls. SIGNIFICANCE: Pelvic obliquity plays an important role in energy recovery during gait for subjects using prosthetics. This information might be exploited to monitor the adaptation of subjects with TFA to prosthetic devices, to lower the energetic cost of walking potentially, and to reduce the long-term risks of secondary physical complications in prosthetic users.

Global Muscle Coactivation of the Sound Limb in Gait of People with Transfemoral and Transtibial Amputation.

The aim of this study was to analyze the effect of the level of amputation and various prosthetic devices on the muscle activation of the sound limb in people with unilateral transfemoral and transtibial amputation. We calculated the global coactivation of 12 muscles using the time-varying multimuscle coactivation function method in 37 subjects with unilateral transfemoral amputation (10, 16, and 11 with mechanical, electronic, and bionic prostheses, respectively), 11 subjects with transtibial amputation, and 22 healthy subjects representing the control group. The results highlighted that people with amputation had a global coactivation temporal profile similar to that of healthy subjects. However, amputation increased the level of the simultaneous activation of many muscles during the loading response and push-off phases of the gait cycle and decreased it in the midstance and swing subphases. This increased coactivation probably plays a role in prosthetic gait asymmetry and energy consumption. Furthermore, people with amputation and wearing electronic prosthesis showed lower global coactivation when compared with people wearing mechanical and bionic prostheses. These findings suggest that the global lower limb coactivation behavior can be a useful tool to analyze the motor control strategies adopted and the ability to adapt to the prosthetic device.

Changes in leg cycling muscle synergies after training augmented by functional electrical stimulation in subacute stroke survivors: a pilot study.

BACKGROUND: Muscle synergies analysis can provide a deep understanding of motor impairment after stroke and of changes after rehabilitation. In this study, the neuro-mechanical analysis of leg cycling was used to longitudinally investigate the motor recovery process coupled with cycling training augmented by Functional Electrical Stimulation (FES) in subacute stroke survivors. METHODS: Subjects with ischemic subacute stroke participated in a 3-week training of FES-cycling with visual biofeedback plus usual care. Participants were evaluated before and after the intervention through clinical scales, gait spatio-temporal parameters derived from an instrumented mat, and a voluntary pedaling test. Biomechanical metrics (work produced by the two legs, mechanical effectiveness and symmetry indexes) and bilateral electromyography from 9 leg muscles were acquired during the voluntary pedaling test. To extract muscles synergies, the Weighted Nonnegative Matrix Factorization algorithm was applied to the normalized EMG envelopes. Synergy complexity was measured by the number of synergies required to explain more than 90% of the total variance of the normalized EMG envelopes and variance accounted for by one synergy. Regardless the inter-subject differences in the number of extracted synergies, 4 synergies were extracted from each patient and the cosine-similarity between patients and healthy weight vectors was computed. RESULTS: Nine patients (median age of 75 years and median time post-stroke of 2 weeks) were recruited. Significant improvements in terms of clinical scales, gait parameters and work produced by the affected leg were obtained after training. Synergy complexity well correlated to the level of motor impairment at baseline, but it did not change after training. We found a significant improvement in the similarity of the synergy responsible of the knee flexion during the pulling phase of the pedaling cycle, which was the mostly compromised at baseline. This improvement may indicate the re-learning of a more physiological motor strategy. CONCLUSIONS: Our findings support the use of the neuro-mechanical analysis of cycling as a method to assess motor recovery after stroke, mainly in an early phase, when gait evaluation is not yet possible. The improvement in the modular coordination of pedaling correlated with the improvement in motor functions and walking ability achieved at the end of the intervention support the role of FES-cycling in enhancing motor re-learning after stroke but need to be confirmed in a controlled study with a larger sample size. TRIAL REGISTRATION: ClinicalTrial.gov, NCT02439515. Registered on May 8, 2015, .

Modular motor control of the sound limb in gait of people with trans-femoral amputation.

BACKGROUND: The above-knee amputation of a lower limb is a severe impairment that affects significantly the ability to walk; considering this, a complex adaptation strategy at the neuromuscular level is needed in order to be able to move safely with a prosthetic knee. In literature, it has been demonstrated that muscle activity during walking can be described via the activation of a small set of muscle synergies. The analysis of the composition and the time activation profiles of such synergies have been found to be a valid tool for the description of the motor control schemes in pathological subjects. METHODS: In this study, we used muscle synergy analysis techniques to characterize the differences in the modular motor control schemes between a population of 14 people with trans-femoral amputation and 12 healthy subjects walking at two different (slow and normal self-selected) speeds. Muscle synergies were extracted from a 12 lower-limb muscles sEMG recording via non-negative matrix factorization. Equivalence of the synergy vectors was quantified by a cross-validation procedure, while differences in terms of time activation coefficients were evaluated through the analysis of the activity in the different gait sub-phases. RESULTS: Four synergies were able to reconstruct the muscle activity in all subjects. The spatial component of the synergy vectors did not change in all the analysed populations, while differences were present in the activity during the sound limb's stance phase. Main features of people with trans-femoral amputation's muscle synergy recruitment are a prolonged activation of the module composed of calf muscles and an additional activity of the hamstrings' module before and after the prosthetic heel strike. CONCLUSIONS: Synergy-based results highlight how, although the complexity and the spatial organization of motor control schemes are the same found in healthy subjects, substantial differences are present in the synergies' recruitment of people with trans femoral amputation. In particular, the most critical task during the gait cycle is the weight transfer from the sound limb to the prosthetic one. Future studies will integrate these results with the dynamics of movement, aiming to a complete neuro-mechanical characterization of people with trans-femoral amputation's walking strategies that can be used to improve the rehabilitation therapies.

Optimizing the Scale of a Wavelet-Based Method for the Detection of Gait Events from a Waist-Mounted Accelerometer under Different Walking Speeds.

The accurate and reliable extraction of specific gait events from a single inertial sensor at waist level has been shown to be challenging. Among several techniques, a wavelet-based method for initial contact (IC) and final contact (FC) estimation was shown to be the most accurate in healthy subjects. In this study, we evaluated the sensitivity of events detection to the wavelet scale of the algorithm, when walking at different speeds, in order to optimize its selection. A single inertial sensor recorded the lumbar vertical acceleration of 20 subjects walking at three different self-selected speeds (slow, normal, and fast) in a motion analysis lab. The scale of the wavelet method was varied. ICs were generally accurately detected in a wide range of wavelet scales under all the walking speeds. FCs detection proved highly sensitive to scale choice. Different gait speeds required the selection of a different scale for accurate detection and timing, with the optimal scale being strongly correlated with subjects' step frequency. The best speed-dependent scales of the algorithm led to highly accurate timing in the detection of IC (RMSE < 22 ms) and FC (RMSE < 25 ms) across all speeds. Our results pave the way for the optimal adaptive selection of scales in future applications using this algorithm.

Common and specific gait patterns in people with varying anatomical levels of lower limb amputation and different prosthetic components.

The present study's aim was to identify the kinematic and kinetic gait patterns and to measure the energy consumption in people with amputation according to both the anatomical level of amputation and the type of prosthetic components in comparison with a control group matched for the gait speed. Fifteen subjects with unilateral transtibial amputation (TTA), forty with unilateral transfemoral amputation (TFA) (9 with mechanical, 17 with CLeg and 14 with Genium prosthesis) and forty healthy subjects were recruited. We computed the time-distance gait parameters; the range of angular motion (RoM) at hip, knee and ankle joints, and at the trunk and pelvis; the values of the 2 peaks of vertical force curve; the full width at half maximum (FWHM) and center of activity (CoA) of vertical force; the mechanical behavior in terms of energy recovery (R-step) and energy consumption. The main results were: i) both TTA and TFA show a common gait pattern characterized by a symmetric increase of step length, step width, double support duration, pelvic obliquity, trunk lateral bending and trunk rotation RoMs compared to control groups. They show also an asymmetric increase of stance duration and of Peak1 in non-amputated side and a decrease of ankle RoM in amputated side; ii) only TFA show a specific gait pattern, depending on the level of amputation, characterized by a symmetric reduction of R-step and an asymmetric decrease of stance duration, CoA and FWHM and an increase of Peak1 in the amputated side and of hip and knee RoM, CoA and FWHM in the non-amputated side; iii) people with amputation with Genium prosthesis show a longer step length and increased hip and knee RoMs compared to people with amputation with mechanical prosthesis who conversely show an increased pelvic obliquity: these are specific gait patterns depending of the type of prosthesis. In conclusion, we identified both common and specific gait patterns in people with amputation, either regardless of, or according to their level of amputation and the type of prosthetic component.

Consistent visuomotor adaptations and generalizations can be achieved through different rotations of robust motor modules.

Humans can adapt their motor commands in response to alterations in the movement environment. This is achieved by tuning different motor primitives, generating adaptations that can be generalized also to relevant untrained scenarios. A theory of motor primitives has shown that natural movements can be described as combinations of muscle synergies. Previous studies have shown that motor adaptations are achieved by tuning the recruitment of robust synergy modules. Here we tested if: 1) different synergistic tunings can be achieved in response to the same perturbations applied with different orders of exposure; 2) different synergistic tunings can explain different patterns of generalization of adaptation. We found that exposing healthy individuals to two visuomotor rotation perturbations covering different parts of the same workspace in a different order resulted in different tunings of the activation of the same set of synergies. Nevertheless, these tunings resulted in the same net biomechanical adaptation patterns. We also show that the characteristics of the different tunings correlate with the presence and extent of generalization of adaptation to untrained portions of the workspace. Our results confirm synergies as invariant motor primitives whose recruitment is dynamically tuned during motor adaptations.

Effect of Task Failure on Intermuscular Coherence Measures in Synergistic Muscles.

The term "task failure" describes the point when a person is not able to maintain the level of force required by a task. As task failure approaches, the corticospinal command to the muscles increases to maintain the required level of force in the face of a decreased mechanical efficacy. Nevertheless, most motor tasks require the synergistic recruitment of several muscles. How this recruitment is affected by approaching task failure is still not clear. The increase in the corticospinal drive could be due to an increase in synergistic recruitment or to overlapping commands sent to the muscles individually. Herein, we investigated these possibilities by combining intermuscular coherence and synergy analysis on signals recorded from three muscles of the quadriceps during dynamic leg extension tasks. We employed muscle synergy analysis to investigate changes in the coactivation of the muscles. Three different measures of coherence were used. Pooled coherence was used to estimate the command synchronous to all three muscles, pairwise coherence the command shared across muscle pairs and residual coherence the command peculiar to each couple of muscles. Our analysis highlights an overall decrease in synergistic command at task failure and an intensification of the contribution of the nonsynergistic shared command.

Comparison of Initialization Techniques for the Accurate Extraction of Muscle Synergies from Myoelectric Signals via Nonnegative Matrix Factorization.

The main goal of this work was to assess the performance of different initializations of matrix factorization algorithms for an accurate identification of muscle synergies. Currently, nonnegative matrix factorization (NNMF) is the most commonly used method to identify muscle synergies. However, it has been shown that NNMF performance might be affected by different kinds of initialization. The present study aims at optimizing the traditional NNMF initialization for data with partial or complete temporal dependencies. For this purpose, three different initializations are used: random, SVD-based, and sparse. NNMF was used to identify muscle synergies from simulated data as well as from experimental surface EMG signals. Simulated data were generated from synthetic independent and dependent synergy vectors (i.e., shared muscle components), whose activation coefficients were corrupted by simulating controlled degrees of correlation. Similarly, EMG data were artificially modified, making the extracted activation coefficients temporally dependent. By measuring the quality of identification of the original synergies underlying the data, it was possible to compare the performance of different initialization techniques. Simulation results demonstrate that sparse initialization performs significantly better than all other kinds of initialization in reconstructing muscle synergies, regardless of the correlation level in the data.